Optimal learning rate scaling in deep networks depends on data, research finds

A new research note explores the dynamics of deep scalar linear networks, demonstrating that optimal learning rate scaling is data-dependent. The study shows that data-agnostic scaling rules falter across different network depths. However, when optimal data-dependent scaling is applied, learning dynamics become independent of the data and only weakly dependent on depth, leading to consistent linear convergence rates across all depths, including infinite depth. This data-dependent effect was also observed in networks incorporating residual connections. AI

IMPACT Provides theoretical insights into optimizing training dynamics for deep learning models.

RANK_REASON Research paper published on arXiv detailing findings about deep learning networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Deep Scalar Linear Networks
• gradient descent
• Hugging Face
• Learning Rate Scaling
• Residual Connections

• paper
• other

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